Browse
Submit Data
Databases
API
Help

Dataset Information

25 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Controlling the thermal conductivity of multilayer graphene by strain.

ABSTRACT: Straintronics is a new concept to enhance electronic device performances by strain for next-generation information sensors and energy-saving technologies. The lattice deformation in graphene can modulate the thermal conductivity because phonons are the main heat carriers. However, the device fabrication process affects graphene's heat transport properties due to its high stretchability. This study experimentally investigates the change in the thermal conductivity when biaxial tensile strain is applied to graphene. To eliminate non-strain factors, two mechanisms are considered: pressure-induced and electrostatic attraction-induced strain. Raman spectroscopy and atomic force microscopy precisely estimate the strain. The thermal conductivity of graphene decreases by approximately 70% with a strain of only 0.1%. Such thermal conductivity controllability paves the way for applying graphene as high-efficiency thermal switches and diodes in future thermal management devices.

SUBMITTER: Nakagawa K

PROVIDER: S-EPMC8486806 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Properties of Graphene-Related Materials Controlling the Thermal Conductivity of Their Polymer Nanocomposites.

Project description:Different types of graphene-related materials (GRM) are industrially available and have been exploited for thermal conductivity enhancement in polymers. These include materials with very different features, in terms of thickness, lateral size and composition, especially concerning the oxygen to carbon ratio and the possible presence of surface functionalization. Due to the variability of GRM properties, the differences in polymer nanocomposites preparation methods and the microstructures obtained, a large scatter of thermal conductivity performance is found in literature. However, detailed correlations between GRM-based nanocomposites features, including nanoplatelets thickness and size, defectiveness, composition and dispersion, with their thermal conductivity remain mostly undefined. In the present paper, the thermal conductivity of GRM-based polymer nanocomposites, prepared by melt polymerization of cyclic polybutylene terephtalate oligomers and exploiting 13 different GRM grades, was investigated. The selected GRM, covering a wide range of specific surface area, size and defectiveness, secure a sound basis for the understanding of the effect of GRM properties on the thermal conductivity of their relevant polymer nanocomposites. Indeed, the obtained thermal conductivity appeares to depend on the interplay between the above GRM feature. In particular, the combination of low GRM defectiveness and high filler percolation density was found to maximize the thermal conductivity of nanocomposites.

| S-EPMC7692405 | biostudies-literature

Large-Area Thermal Distribution Sensor Based on Multilayer Graphene Ink.

Project description:Emergent applications in wearable electronics require inexpensive sensors suited to scalable manufacturing. This work demonstrates a large-area thermal sensor based on distributed thermocouple architecture and ink-based multilayer graphene film. The proposed device combines the exceptional mechanical properties of multilayer graphene nanocomposite with the reliability and passive sensing performance enabled by thermoelectrics. The Seebeck coefficient of the spray-deposited films revealed an inverse thickness dependence with the largest value of 44.7 ?V K-1 at 78 nm, which makes thinner films preferable for sensor applications. Device performance was demonstrated by touch sensing and thermal distribution mapping-based shape detection. Sensor output voltage in the latter application was on the order of 300 ?V with a signal-to-noise ratio (SNR) of 35, thus enabling accurate detection of objects of different shapes and sizes. The results imply that films based on multilayer graphene ink are highly suitable to thermoelectric sensing applications, while the ink phase enables facile integration into existing fabrication processes.

| S-EPMC7570513 | biostudies-literature

Strain-controlled thermal conductivity in ferroic twinned films.

Project description:Large reversible changes of thermal conductivity are induced by mechanical stress, and the corresponding device is a key element for phononics applications. We show that the thermal conductivity κ of ferroic twinned thin films can be reversibly controlled by strain. Nonequilibrium molecular dynamics simulations reveal that thermal conductivity decreases linearly with the number of twin boundaries perpendicular to the direction of heat flow. Our demonstration of large and reversible changes in thermal conductivity driven by strain may inspire the design of controllable thermal switches for thermal logic gates and all-solid-state cooling devices.

| S-EPMC4165272 | biostudies-literature

Enhanced Thermal Conductivity in Oriented Polyvinyl Alcohol/Graphene Oxide Composites.

Project description:Polymer composites have attracted increasing interest as thermal management materials for use in devices owing to their ease of processing and potential lower costs. However, most polymer composites have only modest thermal conductivities, even at high concentrations of additives, resulting in high costs and reduced mechanical properties, which limit their applications. To achieve high thermally conductive polymer materials with a low concentration of additives, anisotropic, solid-state drawn composite films were prepared using water-soluble polyvinyl alcohol (PVA) and dispersible graphene oxide (GO). A co-additive (sodium dodecyl benzenesulfonate) was used to improve both the dispersion of GO and consequently the thermal conductivity. The hydrogen bonding between GO and PVA and the simultaneous alignment of GO and PVA in drawn composite films contribute to an improved thermal conductivity (∼25 W m-1 K-1), which is higher than most reported polymer composites and an approximately 50-fold enhancement over isotropic PVA (0.3-0.5 W m-1 K-1). This work provides a new method for preparing water-processable, drawn polymer composite films with high thermal conductivity, which may be useful for thermal management applications.

| S-EPMC8289248 | biostudies-literature

Model Approach to Thermal Conductivity in Hybrid Graphene-Polymer Nanocomposites.

Project description:The thermal conductivity of epoxy nanocomposites filled with self-assembled hybrid nanoparticles composed of multilayered graphene nanoplatelets and anatase nanoparticles was described using an analytical model based on the effective medium approximation with a reasonable amount of input data. The proposed effective thickness approach allowed for the simplification of the thermal conductivity simulations in hybrid graphene@anatase TiO2 nanosheets by including the phenomenological thermal boundary resistance. The sensitivity of the modeled thermal conductivity to the geometrical and material parameters of filling particles and the host polymer matrix, filler's mass concentration, self-assembling degree, and Kapitza thermal boundary resistances at emerging interfaces was numerically evaluated. A fair agreement of the calculated and measured room-temperature thermal conductivity was obtained.

| S-EPMC10647783 | biostudies-literature

Experimental Control and Statistical Analysis of Thermal Conductivity in ZnO-Benzene Multilayer Thin Films.

Project description:We have fabricated a model system of precisely layer-engineered inorganic-organic thin-film structures using atomic/molecular-layer deposition (ALD/MLD). The samples consist of nanoscale polycrystalline ZnO layers and intervening benzene layers, covering a broad range of layer sequences. The samples characterized in this study combined with previous publications provide an excellent sample set to examine thermal transport properties in inorganic-organic thin films. The cross-plane thermal conductivity is found to depend on multiple factors, with the inorganic-organic interface density being the dominating factor. Our work highlights the remarkable capability of interface engineering in suppressing the thermal conductivity of hybrid inorganic-organic materials, e.g., for thermoelectric applications.

| S-EPMC7667989 | biostudies-literature

Ultralow Lattice Thermal Conductivity of the Random Multilayer Structure with Lattice Imperfections.

Project description:Randomizing the layer thickness of superlattices (SL) can lead to localization of coherent phonons and thereby reduces the lattice thermal conductivity κ l . In this work, we propose strategies that can suppress incoherent phonon transport in the above random multilayer (RML) structure to further reduce κ l . Molecular dynamics simulations are conducted to investigate phonon heat conduction in SLs and RMLs with lattice imperfections. We found that interfacial species mixing enhances thermal transport across single interfaces and few-period SLs through the phonon "bridge" mechanism, while it substantially reduces the κ l of many-period SLs by breaking the phonon coherence. This is a clear manifestation of the transition from incoherent-phonon-dominated to coherent-phonon-dominated heat conduction in SLs when the number of interface increases. In contrast, interfacial species mixing always increases the κ l of RMLs owing to the dominance of incoherent phonons. Moreover, we found that doping a binary RML with impurities can reduce κ l significantly, especially when the impurity atom has an atomic mass lower or higher than both of the two base elements. This work reveals the critical effect of lattice imperfections on thermal transport in SLs and RMLs, and provides a unique strategy to hierachically suppress coherent and incoherent phonon transport concurrently.

| S-EPMC5557814 | biostudies-other

Investigations for Thermal and Electrical Conductivity of ABS-Graphene Blended Prototypes.

Project description:The thermoplastic materials such as acrylonitrile-butadiene-styrene (ABS) and Nylon have large applications in three-dimensional printing of functional/non-functional prototypes. Usually these polymer-based prototypes are lacking in thermal and electrical conductivity. Graphene (Gr) has attracted impressive enthusiasm in the recent past due to its natural mechanical, thermal, and electrical properties. This paper presents the step by step procedure (as a case study) for development of an in-house ABS-Gr blended composite feedstock filament for fused deposition modelling (FDM) applications. The feedstock filament has been prepared by two different methods (mechanical and chemical mixing). For mechanical mixing, a twin screw extrusion (TSE) process has been used, and for chemical mixing, the composite of Gr in an ABS matrix has been set by chemical dissolution, followed by mechanical blending through TSE. Finally, the electrical and thermal conductivity of functional prototypes prepared from composite feedstock filaments have been optimized.

| S-EPMC5578247 | biostudies-literature

Thermal Percolation Behavior in Thermal Conductivity of Polymer Nanocomposite with Lateral Size of Graphene Nanoplatelet.

Project description:In this study, the thermal percolation behavior for the thermal conductivity of nanocomposites according to the lateral size of graphene nanoplatelets (GNPs) was studied. When the amount of GNPs reached the critical concentration, a rapid increase in thermal conductivity and thermal percolation behavior of the nanocomposites were induced by the GNP network. Interestingly, as the size of GNPs increased, higher thermal conductivity and a lower percolation threshold were observed. The in-plane thermal conductivity of the nanocomposite containing 30 wt.% M25 GNP (the largest size) was 8.094 W/m·K, and it was improved by 1518.8% compared to the polymer matrix. These experimentally obtained thermal conductivity results for below and above the critical content were theoretically explained by applying Nan's model and the percolation model, respectively, in relation to the GNP size. The thermal percolation behavior according to the GNP size identified in this study can provide insight into the design of nanocomposite materials with excellent heat dissipation properties.

| S-EPMC8780660 | biostudies-literature

Thermal conductivity of polymer composites with the geometrical characteristics of graphene nanoplatelets.

Project description:One of the most important physical factors related to the thermal conductivity of composites filled with graphene nanoplatelets (GNPs) is the dimensions of the GNPs, that is, their lateral size and thickness. In this study, we reveal the relationship between the thermal conductivity of polymer composites and the realistic size of GNP fillers within the polymer composites (measured using three-dimensional (3D) non-destructive micro X-ray CT analysis) while minimizing the effects of the physical parameters other than size. A larger lateral size and thickness of the GNPs increased the likelihood of the matrix-bonded interface being reduced, resulting in an effective improvement in the thermal conductivity and in the heat dissipation ability of the composites. The thermal conductivity was improved by up to 121% according to the filler size; the highest bulk and in-plane thermal conductivity values of the composites filled with 20?wt% GNPs were 1.8 and 7.3?W/m·K, respectively. The bulk and in-plane thermal conductivity values increased by 650 and 2,942%, respectively, when compared to the thermal conductivity values of the polymer matrix employed (0.24?W/m·K).

| S-EPMC4879581 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data